{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6c33f95-853e-4bdb-9c5a-7fce5aeb50b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16 {0: [(1.0, 0, 0.0, False)], 1: [(1.0, 4, 0.0, False)], 2: [(1.0, 1, 0.0, False)], 3: [(1.0, 0, 0.0, False)]}\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.] [[0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\16673\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backends is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n",
      "c:\\Users\\16673\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\IPython\\core\\pylabtools.py:77: DeprecationWarning: backend2gui is deprecated since IPython 8.24, backends are managed in matplotlib and can be externally registered.\n",
      "  warnings.warn(\n",
      "c:\\Users\\16673\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\gym\\core.py:317: DeprecationWarning: \u001b[33mWARN: Initializing wrapper in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
      "  deprecation(\n",
      "c:\\Users\\16673\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\gym\\wrappers\\step_api_compatibility.py:39: DeprecationWarning: \u001b[33mWARN: Initializing environment in old step API which returns one bool instead of two. It is recommended to set `new_step_api=True` to use new step API. This will be the default behaviour in future.\u001b[0m\n",
      "  deprecation(\n"
     ]
    }
   ],
   "source": [
    "import gym\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "import os\n",
    "\n",
    "os.environ['SDL_VIDEODRIVER']='dummy' # 隐藏窗口\n",
    "\n",
    "# 创建环境\n",
    "# is_slippery=False ：不考虑冰上的滑水\n",
    "# map_name='4x4' ：4x4的冰湖\n",
    "# desc决定地形\n",
    "\n",
    "ROWS, COLS = 4, 4\n",
    "\n",
    "env = gym.make('FrozenLake-v1', is_slippery=False,\n",
    "               map_name=F'{ROWS}x{COLS}',  # 修正：添加缺失的引号\n",
    "               desc=['SFFF',\n",
    "                     'FHFH',\n",
    "                     'FFFH',\n",
    "                     'HFFG'])\n",
    "env.reset() # 重置环境\n",
    "\n",
    "# 解封装才能访问状态转移矩阵P\n",
    "env = env.unwrapped\n",
    "\n",
    "def show():\n",
    "    plt.imshow(env.render('rgb_array'))\n",
    "    plt.show()\n",
    "\n",
    "# 查看冰湖这个游戏的状态列表\n",
    "# 一共4*4=16个状态\n",
    "# 每个状态下都可以进行4个动作\n",
    "# 每个动作执行完，都有概率得到3个结果\n",
    "# (0.333333333333, 0, 0, 0, False)这个数据结构表示（概率， 下一个状态，奖励，是否结束）\n",
    "\n",
    "print(len(env.P), env.P[0])\n",
    "\n",
    "# show()\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "# 初始化每个格子的价值\n",
    "values = np.zeros(16)\n",
    "\n",
    "# 初始化每个格子下采用动作的概率\n",
    "pi = np.ones([16, 4]) * 0.25 # 每个状态下，每个动作的概率都是0.25\n",
    "\n",
    "# 两个算法都是可以的，但是价值迭代的速度更快\n",
    "algorithm = '策略迭代'\n",
    "algorithm = '价值迭代'\n",
    "\n",
    "print(values, pi)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bd3c75ce-1f2a-4282-a7c2-fa2375ba6a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "0.0 0.0 0.0 0.0 \n",
      "0.0 0.0 0.0 0.0 \n",
      "0.0 0.0 0.0 0.0 \n",
      "0.0 0.0 0.0 0.0 \n"
     ]
    }
   ],
   "source": [
    "# 计算qsa\n",
    "def get_qsa(state, action):\n",
    "    \"\"\"\n",
    "    获取状态s下，动作a的价值\n",
    "    \"\"\"\n",
    "    value = 0\n",
    "\n",
    "    # 每个动作都会有三个不同的结果，这里要按概率把他们加权求和\n",
    "    for prop, next_state, reward, over in env.P[state][action]:\n",
    "\n",
    "        #计算下个状态的分数，取values当中记录的分数即可，0.9是折扣因子\n",
    "        next_value = values[next_state] * 0.9\n",
    "\n",
    "        # 如果下个状态是终点或者陷阱，则下一个状态的分数是0\n",
    "        if over:\n",
    "            next_value = 0\n",
    "\n",
    "        # 获取当前状态的分数\n",
    "        next_value += reward\n",
    "\n",
    "        # 因为下个状态是概率出现了，所以要乘以概率\n",
    "        next_value *= prop\n",
    "\n",
    "        value += next_value\n",
    "\n",
    "    return value\n",
    "\n",
    "print(get_qsa(0, 0)) # 0状态下，0动作的价值\n",
    "\n",
    "for r in range(4):\n",
    "    for c in range(4):\n",
    "        print(get_qsa(r, c), end=' ')\n",
    "    print()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8bbc4dc8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n"
     ]
    }
   ],
   "source": [
    "# 策略评估\n",
    "def get_value():\n",
    "    # 初始化一个新的values，重新评估所有格子的分数\n",
    "    new_values = np.zeros([16])\n",
    "\n",
    "    # 遍历所有格子\n",
    "    for state in range(16):\n",
    "        # 计算当前格子4个动作分别的分数\n",
    "        action_value = np.zeros(4)\n",
    "\n",
    "        # 遍历所有动作\n",
    "        for action in range(4):\n",
    "            action_value[action] = get_qsa(state, action)\n",
    "        \n",
    "        if algorithm == '策略迭代':\n",
    "            # 每个动作的分数和它的概率相乘\n",
    "            action_value  *= pi[state]\n",
    "            # 最后这个格子的分数，等于该格子下所有动作的分数求和\n",
    "            new_values[state] = action_value.sum()\n",
    "        \n",
    "        if algorithm == '价值迭代':\n",
    "            # 价值迭代，直接取最大的分数\n",
    "            new_values[state] = action_value.max()\n",
    "    \n",
    "    return new_values\n",
    "\n",
    "print(get_value())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "53801304",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.   0.   1.   0.  ]\n",
      " [0.25 0.25 0.25 0.25]]\n"
     ]
    }
   ],
   "source": [
    "# 策略提升\n",
    "def get_pi():\n",
    "    # 重新初始化每个格子下采用动作的概率，重新评估\n",
    "    new_pi = np.zeros([16, 4])\n",
    "\n",
    "    # 遍历所有动作\n",
    "    for state in range(16):\n",
    "        # 计算当前格子4个动作分别的分数\n",
    "        action_value = np.zeros(4)\n",
    "\n",
    "        #遍历所有动作\n",
    "        for action in range(4):\n",
    "            action_value[action] = get_qsa(state, action)\n",
    "        \n",
    "        # 在每个状态内部计算count\n",
    "        count = (action_value == action_value.max()).sum() # 概率 = 当前格子下，该动作的分数除以所有动作的分数之和\n",
    "\n",
    "        # 让这些动作均分概率\n",
    "        for action in range(4):\n",
    "            if action_value[action] == action_value.max(): # 如果当前行动值 == 行动表最大值\n",
    "                new_pi[state, action] = 1 / count # 均分概率\n",
    "            else:\n",
    "                new_pi[state, action] = 0\n",
    "\n",
    "    return new_pi\n",
    "\n",
    "print(get_pi())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "87eb9e05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.59049 0.6561  0.729   0.6561  0.6561  0.      0.81    0.      0.729\n",
      " 0.81    0.9     0.      0.      0.9     1.      0.     ] [[0.   0.5  0.5  0.  ]\n",
      " [0.   0.   1.   0.  ]\n",
      " [0.   1.   0.   0.  ]\n",
      " [1.   0.   0.   0.  ]\n",
      " [0.   1.   0.   0.  ]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.   1.   0.   0.  ]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.   0.   1.   0.  ]\n",
      " [0.   0.5  0.5  0.  ]\n",
      " [0.   1.   0.   0.  ]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.25 0.25 0.25 0.25]\n",
      " [0.   0.   1.   0.  ]\n",
      " [0.   0.   1.   0.  ]\n",
      " [0.25 0.25 0.25 0.25]]\n"
     ]
    }
   ],
   "source": [
    "for _ in range(1000):\n",
    "    values = get_value()\n",
    "\n",
    "pi = get_pi()\n",
    "\n",
    "print(values, pi)\n",
    "\n",
    "from IPython import display\n",
    "import time\n",
    "\n",
    "def play():\n",
    "    env.reset()\n",
    "\n",
    "    # 起点0\n",
    "    index = 0\n",
    "\n",
    "    # 最多玩N步\n",
    "    for i in range(100):\n",
    "        # 选择一个动作\n",
    "        action = np.random.choice(4, size=1, p=pi[index])[0]\n",
    "\n",
    "        # 执行动作\n",
    "        index, reward, terminated, truncated, _ = env.step(action)\n",
    "\n",
    "        display.clear_output(wait=True)\n",
    "        time.sleep(0.2)\n",
    "        show()\n",
    "\n",
    "        # 获取当前状态，如果状态时中带你或者掉进陷阱则终止\n",
    "        if terminated or truncated:\n",
    "            break\n",
    "    \n",
    "    print(i)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a863aeea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "play()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
